<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expression and microarrays.</style></title><secondary-title><style face="normal" font="default" size="100%">Methods Mol Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Methods Mol Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">453</style></volume><pages><style face="normal" font="default" size="100%">245-55</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;High throughput methodologies have increased by several orders of magnitude the amount of experimental microarray data available. Nevertheless, translating these data into useful biological knowledge remains a challenge. There is a risk of perceiving these methodologies as mere factories that produce never-ending quantities of data if a proper biological interpretation is not provided. Methods of interpreting these data are continuously evolving. Typically, a simple two-step approach has been used, in which genes of interest are first selected based on thresholds for the experimental values, and then enrichment in biologically relevant terms in the annotations of these genes is analyzed in a second step. For various reasons, such methods are quite poor in terms of performance and new procedures inspired by systems biology that directly address sets of functionally related genes are currently under development.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18712307?dopt=Abstract</style></custom1></record></records></xml>